Role & responsibilities

Key Responsibilities

- Lead the end-to-end delivery of AI/ML and LLM-based solutions: problem framing, data exploration, feature engineering, model development, evaluation, deployment, and monitoring.
- Architect and implement advanced generative AI and agentic applications, including prompt engineering, fine-tuning, embedding, vector search, and retrieval-augmented generation (RAG).
- Define and drive best practices for model evaluation, benchmarking, error taxonomy, explainability, and anomaly detection.
- Build and maintain knowledge graphs, graph-based reasoning engines, and semantic search capabilities to power intelligent applications.
- Partner with engineering, SRE, and platform teams to productionize models, optimize for scalability and reliability, and integrate models into enterprise platforms and workflows.
- Ensure compliance with Responsible AI practices, model governance, regulatory requirements, and enterprise MRM controls.
- Drive the creation and adoption of reusable AI assets, experimentation frameworks, and MLOps/LLMOps pipelines.
- Communicate complex technical findings, recommendations, and risks to technical and non-technical stakeholders, including senior leadership.
- Mentor and guide junior and senior data scientists, fostering a culture of innovation, experimentation, and knowledge sharing.
- Stay current with advancements in LLMs, generative AI, agentic systems, and AI/ML engineering to drive pragmatic adoption within the organization.

Required Qualifications

- BS, BTech, or Masters degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field.




- 8+ years of overall experience in IT and 5+ years of relevant experience in machine learning, statistical modeling, and AI solution development, with 2+ years in generative AI/LLMs.
- Proven track record of designing, developing, and operationalizing AI/ML models at enterprise scale.
- Strong proficiency in Python and leading ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras, etc.).
- Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG.
- Experience with both structured and unstructured data, feature engineering, model experimentation, and tuning.
- Solid understanding of model validation, explainability, testing, monitoring, and Responsible AI.
- Proficiency with Azure and/or AWS for AI/ML development and deployment.
- Experience with graph databases and libraries (e.g., Neo4j, DGL), knowledge graphs, and semantic search.
- Strong stakeholder management, communication, and cross-functional collaboration skills.
- Demonstrated ability to work effectively across business, engineering, architecture, and governance teams.

Valuable to have skills

- Experience in financial services, risk, operations, or other regulated enterprise environments.
- Familiarity with model risk management, regulatory compliance, and Responsible AI controls.
- Experience with Databricks, Spark, SQL, LangChain, LlamaIndex, or similar AI/ML engineering toolsets.
- Knowledge of agentic AI, workflow orchestration, and enterprise AI platform capabilities.
- Experience with MLOps/LLMOps, CI/CD, model versioning, experiment tracking, and production monitoring.
- Prior mentoring or team leadership experience.
- Ability to present complex technical concepts in a business-friendly manner.

Preferred candidate profile -

Immediate joiner

📌 Data Scientist (Hyderabad)
🏢 Orcapod Consulting Services
📍 Hyderabad